Overview

Dataset statistics

Number of variables18
Number of observations3000000
Missing cells4725401
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory412.0 MiB
Average record size in memory144.0 B

Variable types

Text6
Numeric6
Categorical5
DateTime1

Alerts

amount_due is highly overall correlated with payment_amountHigh correlation
fine_amount is highly overall correlated with violationHigh correlation
payment_amount is highly overall correlated with amount_dueHigh correlation
violation is highly overall correlated with fine_amountHigh correlation
violation is highly imbalanced (56.2%)Imbalance
issuing_agency is highly imbalanced (90.0%)Imbalance
judgment_entry_date has 2390812 (79.7%) missing valuesMissing
violation_status has 2330658 (77.7%) missing valuesMissing
summons_number has unique valuesUnique
summons_image has unique valuesUnique
reduction_amount has 2433302 (81.1%) zerosZeros
payment_amount has 782730 (26.1%) zerosZeros
amount_due has 2298078 (76.6%) zerosZeros

Reproduction

Analysis started2023-12-05 19:24:09.095634
Analysis finished2023-12-05 19:32:36.556686
Duration8 minutes and 27.46 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

plate
Text

Distinct1380558
Distinct (%)46.0%
Missing14
Missing (%)< 0.1%
Memory size22.9 MiB
2023-12-05T14:32:38.097245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.8697547
Min length1

Characters and Unicode

Total characters20609168
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique867274 ?
Unique (%)28.9%

Sample

1st rowKGY4654
2nd rowRBK1301
3rd rowKNB3224
4th rowKEW1301
5th row8VUB416
ValueCountFrequency (%)
96594mj 644
 
< 0.1%
blankplate 606
 
< 0.1%
2707086 577
 
< 0.1%
97306mc 544
 
< 0.1%
82923me 511
 
< 0.1%
40346jx 511
 
< 0.1%
77582mh 482
 
< 0.1%
60371mk 481
 
< 0.1%
13246mn 473
 
< 0.1%
xx165a 467
 
< 0.1%
Other values (1380544) 2994690
99.8%
2023-12-05T14:32:39.643758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1280783
 
6.2%
2 1272177
 
6.2%
7 1257892
 
6.1%
6 1251032
 
6.1%
3 1247740
 
6.1%
8 1235669
 
6.0%
4 1217978
 
5.9%
5 1215056
 
5.9%
9 1200942
 
5.8%
K 1020460
 
5.0%
Other values (41) 8409439
40.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12143907
58.9%
Uppercase Letter 8465217
41.1%
Other Punctuation 35
 
< 0.1%
Lowercase Letter 5
 
< 0.1%
Math Symbol 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 1020460
 
12.1%
J 646997
 
7.6%
M 604044
 
7.1%
H 519441
 
6.1%
L 410248
 
4.8%
N 397738
 
4.7%
G 380129
 
4.5%
C 371617
 
4.4%
T 341692
 
4.0%
A 338696
 
4.0%
Other values (16) 3434155
40.6%
Decimal Number
ValueCountFrequency (%)
1 1280783
10.5%
2 1272177
10.5%
7 1257892
10.4%
6 1251032
10.3%
3 1247740
10.3%
8 1235669
10.2%
4 1217978
10.0%
5 1215056
10.0%
9 1200942
9.9%
0 964638
7.9%
Other Punctuation
ValueCountFrequency (%)
. 13
37.1%
, 9
25.7%
& 7
20.0%
# 2
 
5.7%
' 1
 
2.9%
@ 1
 
2.9%
; 1
 
2.9%
? 1
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
i 1
20.0%
b 1
20.0%
t 1
20.0%
Math Symbol
ValueCountFrequency (%)
+ 2
66.7%
< 1
33.3%
Open Punctuation
ValueCountFrequency (%)
{ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12143946
58.9%
Latin 8465222
41.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 1020460
 
12.1%
J 646997
 
7.6%
M 604044
 
7.1%
H 519441
 
6.1%
L 410248
 
4.8%
N 397738
 
4.7%
G 380129
 
4.5%
C 371617
 
4.4%
T 341692
 
4.0%
A 338696
 
4.0%
Other values (20) 3434160
40.6%
Common
ValueCountFrequency (%)
1 1280783
10.5%
2 1272177
10.5%
7 1257892
10.4%
6 1251032
10.3%
3 1247740
10.3%
8 1235669
10.2%
4 1217978
10.0%
5 1215056
10.0%
9 1200942
9.9%
0 964638
7.9%
Other values (11) 39
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20609168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1280783
 
6.2%
2 1272177
 
6.2%
7 1257892
 
6.1%
6 1251032
 
6.1%
3 1247740
 
6.1%
8 1235669
 
6.0%
4 1217978
 
5.9%
5 1215056
 
5.9%
9 1200942
 
5.8%
K 1020460
 
5.0%
Other values (41) 8409439
40.8%

state
Text

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:39.932114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6000000
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNY
2nd rowNC
3rd rowNY
4th rowNY
5th rowCA
ValueCountFrequency (%)
ny 2142391
71.4%
nj 279080
 
9.3%
pa 119604
 
4.0%
fl 66134
 
2.2%
ct 57317
 
1.9%
ga 33528
 
1.1%
in 29047
 
1.0%
ma 28521
 
1.0%
tx 28301
 
0.9%
va 26108
 
0.9%
Other values (56) 189969
 
6.3%
2023-12-05T14:32:40.386391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2492272
41.5%
Y 2144290
35.7%
J 279080
 
4.7%
A 245436
 
4.1%
P 120056
 
2.0%
C 107497
 
1.8%
T 100238
 
1.7%
L 85792
 
1.4%
M 72394
 
1.2%
F 66152
 
1.1%
Other values (17) 286793
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5997234
> 99.9%
Decimal Number 2766
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2492272
41.6%
Y 2144290
35.8%
J 279080
 
4.7%
A 245436
 
4.1%
P 120056
 
2.0%
C 107497
 
1.8%
T 100238
 
1.7%
L 85792
 
1.4%
M 72394
 
1.2%
F 66152
 
1.1%
Other values (16) 284027
 
4.7%
Decimal Number
ValueCountFrequency (%)
9 2766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5997234
> 99.9%
Common 2766
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2492272
41.6%
Y 2144290
35.8%
J 279080
 
4.7%
A 245436
 
4.1%
P 120056
 
2.0%
C 107497
 
1.8%
T 100238
 
1.7%
L 85792
 
1.4%
M 72394
 
1.2%
F 66152
 
1.1%
Other values (16) 284027
 
4.7%
Common
ValueCountFrequency (%)
9 2766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2492272
41.5%
Y 2144290
35.7%
J 279080
 
4.7%
A 245436
 
4.1%
P 120056
 
2.0%
C 107497
 
1.8%
T 100238
 
1.7%
L 85792
 
1.4%
M 72394
 
1.2%
F 66152
 
1.1%
Other values (17) 286793
 
4.8%
Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:40.550625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9000000
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPAS
2nd rowPAS
3rd rowPAS
4th rowPAS
5th rowPAS
ValueCountFrequency (%)
pas 2391103
79.7%
com 408767
 
13.6%
omt 79865
 
2.7%
srf 22905
 
0.8%
999 17254
 
0.6%
oms 17136
 
0.6%
app 15882
 
0.5%
lmb 13752
 
0.5%
trc 5648
 
0.2%
mot 5573
 
0.2%
Other values (60) 22115
 
0.7%
2023-12-05T14:32:40.961102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2435744
27.1%
P 2427200
27.0%
A 2407826
26.8%
M 534304
 
5.9%
O 520643
 
5.8%
C 420454
 
4.7%
T 93279
 
1.0%
9 51762
 
0.6%
R 36581
 
0.4%
F 22906
 
0.3%
Other values (14) 49301
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8948238
99.4%
Decimal Number 51762
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2435744
27.2%
P 2427200
27.1%
A 2407826
26.9%
M 534304
 
6.0%
O 520643
 
5.8%
C 420454
 
4.7%
T 93279
 
1.0%
R 36581
 
0.4%
F 22906
 
0.3%
B 18603
 
0.2%
Other values (13) 30698
 
0.3%
Decimal Number
ValueCountFrequency (%)
9 51762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8948238
99.4%
Common 51762
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2435744
27.2%
P 2427200
27.1%
A 2407826
26.9%
M 534304
 
6.0%
O 520643
 
5.8%
C 420454
 
4.7%
T 93279
 
1.0%
R 36581
 
0.4%
F 22906
 
0.3%
B 18603
 
0.2%
Other values (13) 30698
 
0.3%
Common
ValueCountFrequency (%)
9 51762
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 2435744
27.1%
P 2427200
27.0%
A 2407826
26.8%
M 534304
 
5.9%
O 520643
 
5.8%
C 420454
 
4.7%
T 93279
 
1.0%
9 51762
 
0.6%
R 36581
 
0.4%
F 22906
 
0.3%
Other values (14) 49301
 
0.5%

summons_number
Real number (ℝ)

UNIQUE 

Distinct3000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4119661 × 109
Minimum1.0932299 × 109
Maximum9.0874659 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:41.221043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.0932299 × 109
5-th percentile2.0040499 × 109
Q18.8820087 × 109
median8.9737148 × 109
Q39.0409399 × 109
95-th percentile9.0734028 × 109
Maximum9.0874659 × 109
Range7.994236 × 109
Interquartile range (IQR)1.5893115 × 108

Descriptive statistics

Standard deviation1.9066692 × 109
Coefficient of variation (CV)0.22666154
Kurtosis8.0451838
Mean8.4119661 × 109
Median Absolute Deviation (MAD)76864623
Skewness-3.1606275
Sum2.5235898 × 1016
Variance3.6353873 × 1018
MonotonicityNot monotonic
2023-12-05T14:32:41.483801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8827898402 1
 
< 0.1%
8893189010 1
 
< 0.1%
9017795268 1
 
< 0.1%
8915221564 1
 
< 0.1%
9017797046 1
 
< 0.1%
9017798981 1
 
< 0.1%
9017799262 1
 
< 0.1%
9046810860 1
 
< 0.1%
9042637572 1
 
< 0.1%
9050070280 1
 
< 0.1%
Other values (2999990) 2999990
> 99.9%
ValueCountFrequency (%)
1093229913 1
< 0.1%
1131597850 1
< 0.1%
1131623435 1
< 0.1%
1157236935 1
< 0.1%
1253012726 1
< 0.1%
1253014735 1
< 0.1%
1253081980 1
< 0.1%
1256443670 1
< 0.1%
1256444765 1
< 0.1%
1256444790 1
< 0.1%
ValueCountFrequency (%)
9087465919 1
< 0.1%
9087465816 1
< 0.1%
9087465749 1
< 0.1%
9087465567 1
< 0.1%
9087465397 1
< 0.1%
9087465270 1
< 0.1%
9087465166 1
< 0.1%
9087465075 1
< 0.1%
9087455690 1
< 0.1%
9087455677 1
< 0.1%
Distinct1064
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:41.846994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters30000000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row05/21/2021
2nd row05/25/2021
3rd row10/07/2021
4th row05/25/2021
5th row05/26/2021
ValueCountFrequency (%)
11/25/2022 11452
 
0.4%
03/11/2021 9757
 
0.3%
11/26/2021 9592
 
0.3%
08/17/2023 9329
 
0.3%
08/22/2023 9273
 
0.3%
03/12/2021 9153
 
0.3%
05/23/2023 9058
 
0.3%
05/30/2023 8788
 
0.3%
05/19/2023 8707
 
0.3%
05/25/2023 8504
 
0.3%
Other values (1054) 2906387
96.9%
2023-12-05T14:32:42.432119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8615284
28.7%
0 6738708
22.5%
/ 6000000
20.0%
1 3371648
 
11.2%
3 1701709
 
5.7%
8 693245
 
2.3%
5 625811
 
2.1%
7 614944
 
2.0%
6 607013
 
2.0%
9 534303
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24000000
80.0%
Other Punctuation 6000000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8615284
35.9%
0 6738708
28.1%
1 3371648
 
14.0%
3 1701709
 
7.1%
8 693245
 
2.9%
5 625811
 
2.6%
7 614944
 
2.6%
6 607013
 
2.5%
9 534303
 
2.2%
4 497335
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 6000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8615284
28.7%
0 6738708
22.5%
/ 6000000
20.0%
1 3371648
 
11.2%
3 1701709
 
5.7%
8 693245
 
2.3%
5 625811
 
2.1%
7 614944
 
2.0%
6 607013
 
2.0%
9 534303
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8615284
28.7%
0 6738708
22.5%
/ 6000000
20.0%
1 3371648
 
11.2%
3 1701709
 
5.7%
8 693245
 
2.3%
5 625811
 
2.1%
7 614944
 
2.0%
6 607013
 
2.0%
9 534303
 
1.8%
Distinct1528
Distinct (%)0.1%
Missing30
Missing (%)< 0.1%
Memory size22.9 MiB
2023-12-05T14:32:42.817203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9999977
Min length5

Characters and Unicode

Total characters17999813
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row07:44A
2nd row06:35A
3rd row12:12P
4th row08:55A
5th row08:46A
ValueCountFrequency (%)
08:36a 20117
 
0.7%
08:38a 17262
 
0.6%
08:39a 17208
 
0.6%
08:40a 17057
 
0.6%
08:37a 16634
 
0.6%
08:41a 16213
 
0.5%
08:42a 15926
 
0.5%
08:43a 15107
 
0.5%
08:44a 14583
 
0.5%
08:06a 14555
 
0.5%
Other values (1519) 2835310
94.5%
2023-12-05T14:32:43.400204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3049622
16.9%
: 2999970
16.7%
1 2544579
14.1%
A 2155940
12.0%
2 1155071
 
6.4%
4 938429
 
5.2%
8 897278
 
5.0%
3 855852
 
4.8%
P 844023
 
4.7%
9 842150
 
4.7%
Other values (6) 1716899
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11999876
66.7%
Other Punctuation 2999972
 
16.7%
Uppercase Letter 2999963
 
16.7%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3049622
25.4%
1 2544579
21.2%
2 1155071
 
9.6%
4 938429
 
7.8%
8 897278
 
7.5%
3 855852
 
7.1%
9 842150
 
7.0%
5 802890
 
6.7%
7 501002
 
4.2%
6 413003
 
3.4%
Other Punctuation
ValueCountFrequency (%)
: 2999970
> 99.9%
. 1
 
< 0.1%
/ 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 2155940
71.9%
P 844023
 
28.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14999850
83.3%
Latin 2999963
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3049622
20.3%
: 2999970
20.0%
1 2544579
17.0%
2 1155071
 
7.7%
4 938429
 
6.3%
8 897278
 
6.0%
3 855852
 
5.7%
9 842150
 
5.6%
5 802890
 
5.4%
7 501002
 
3.3%
Other values (4) 413007
 
2.8%
Latin
ValueCountFrequency (%)
A 2155940
71.9%
P 844023
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17999813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3049622
16.9%
: 2999970
16.7%
1 2544579
14.1%
A 2155940
12.0%
2 1155071
 
6.4%
4 938429
 
5.2%
8 897278
 
5.0%
3 855852
 
4.8%
P 844023
 
4.7%
9 842150
 
4.7%
Other values (6) 1716899
9.5%

violation
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
NO PARKING-STREET CLEANING
1734224 
NO PARKING-DAY/TIME LIMITS
763323 
DOUBLE PARKING
371839 
DOUBLE PARKING-MIDTOWN COMML
 
109988
NO PARKING-EXC. HOTEL LOADING
 
11504
Other values (7)
 
9122

Length

Max length30
Median length26
Mean length24.586367
Min length13

Characters and Unicode

Total characters73759102
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOUBLE PARKING
2nd rowDOUBLE PARKING
3rd rowNO PARKING-STREET CLEANING
4th rowNO PARKING-STREET CLEANING
5th rowNO PARKING-STREET CLEANING

Common Values

ValueCountFrequency (%)
NO PARKING-STREET CLEANING 1734224
57.8%
NO PARKING-DAY/TIME LIMITS 763323
25.4%
DOUBLE PARKING 371839
 
12.4%
DOUBLE PARKING-MIDTOWN COMML 109988
 
3.7%
NO PARKING-EXC. HOTEL LOADING 11504
 
0.4%
ANGLE PARKING 4028
 
0.1%
NO PARKING-EXC. HNDICAP PERMIT 2306
 
0.1%
BUS PARKING IN LOWER MANHATTAN 1940
 
0.1%
NO PARKING-EXC. AUTH. VEHICLE 727
 
< 0.1%
ANGLE PARKING-COMM VEHICLE 81
 
< 0.1%
Other values (2) 40
 
< 0.1%

Length

2023-12-05T14:32:43.679636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 2512115
29.1%
cleaning 1734224
20.1%
parking-street 1734224
20.1%
parking-day/time 763323
 
8.8%
limits 763323
 
8.8%
double 481827
 
5.6%
parking 377807
 
4.4%
parking-midtown 109988
 
1.3%
comml 109988
 
1.3%
parking-exc 14537
 
0.2%
Other values (17) 41194
 
0.5%

Most occurring characters

ValueCountFrequency (%)
N 9114339
12.4%
I 7153094
 
9.7%
E 6483852
 
8.8%
5642550
 
7.6%
A 5522075
 
7.5%
T 5123579
 
6.9%
G 4749846
 
6.4%
R 4738488
 
6.4%
O 3238965
 
4.4%
L 3119227
 
4.2%
Other values (16) 18873087
25.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 64715763
87.7%
Space Separator 5642550
 
7.6%
Dash Punctuation 2622193
 
3.6%
Other Punctuation 778596
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9114339
14.1%
I 7153094
11.1%
E 6483852
10.0%
A 5522075
8.5%
T 5123579
7.9%
G 4749846
 
7.3%
R 4738488
 
7.3%
O 3238965
 
5.0%
L 3119227
 
4.8%
P 3004612
 
4.6%
Other values (12) 12467686
19.3%
Other Punctuation
ValueCountFrequency (%)
/ 763332
98.0%
. 15264
 
2.0%
Space Separator
ValueCountFrequency (%)
5642550
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2622193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64715763
87.7%
Common 9043339
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9114339
14.1%
I 7153094
11.1%
E 6483852
10.0%
A 5522075
8.5%
T 5123579
7.9%
G 4749846
 
7.3%
R 4738488
 
7.3%
O 3238965
 
5.0%
L 3119227
 
4.8%
P 3004612
 
4.6%
Other values (12) 12467686
19.3%
Common
ValueCountFrequency (%)
5642550
62.4%
- 2622193
29.0%
/ 763332
 
8.4%
. 15264
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73759102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9114339
12.4%
I 7153094
 
9.7%
E 6483852
 
8.8%
5642550
 
7.6%
A 5522075
 
7.5%
T 5123579
 
6.9%
G 4749846
 
6.4%
R 4738488
 
6.4%
O 3238965
 
4.4%
L 3119227
 
4.2%
Other values (16) 18873087
25.6%

judgment_entry_date
Date

MISSING 

Distinct93
Distinct (%)< 0.1%
Missing2390812
Missing (%)79.7%
Memory size22.9 MiB
Minimum2011-05-19 00:00:00
Maximum2023-11-30 00:00:00
2023-12-05T14:32:43.925892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:44.201725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fine_amount
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.647902
Minimum35
Maximum495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:44.446568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile60
Q165
median65
Q365
95-th percentile115
Maximum495
Range460
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.264458
Coefficient of variation (CV)0.26517569
Kurtosis3.1676852
Mean72.647902
Median Absolute Deviation (MAD)0
Skewness1.9012016
Sum2.179437 × 108
Variance371.11933
MonotonicityNot monotonic
2023-12-05T14:32:44.646016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
65 2090497
69.7%
115 495274
 
16.5%
60 407838
 
13.6%
45 3932
 
0.1%
180 2306
 
0.1%
235 38
 
< 0.1%
335 36
 
< 0.1%
345 21
 
< 0.1%
315 21
 
< 0.1%
165 16
 
< 0.1%
Other values (6) 21
 
< 0.1%
ValueCountFrequency (%)
35 6
 
< 0.1%
45 3932
 
0.1%
60 407838
 
13.6%
65 2090497
69.7%
85 1
 
< 0.1%
95 4
 
< 0.1%
115 495274
 
16.5%
150 1
 
< 0.1%
165 16
 
< 0.1%
180 2306
 
0.1%
ValueCountFrequency (%)
495 6
 
< 0.1%
435 3
 
< 0.1%
345 21
 
< 0.1%
335 36
 
< 0.1%
315 21
 
< 0.1%
235 38
 
< 0.1%
180 2306
 
0.1%
165 16
 
< 0.1%
150 1
 
< 0.1%
115 495274
16.5%

penalty_amount
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
0
1685462 
60
638655 
10
498472 
30
177411 

Length

Max length2
Median length1
Mean length1.4381793
Min length1

Characters and Unicode

Total characters4314538
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
0 1685462
56.2%
60 638655
 
21.3%
10 498472
 
16.6%
30 177411
 
5.9%

Length

2023-12-05T14:32:44.877000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:32:45.112193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1685462
56.2%
60 638655
 
21.3%
10 498472
 
16.6%
30 177411
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 3000000
69.5%
6 638655
 
14.8%
1 498472
 
11.6%
3 177411
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4314538
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3000000
69.5%
6 638655
 
14.8%
1 498472
 
11.6%
3 177411
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4314538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3000000
69.5%
6 638655
 
14.8%
1 498472
 
11.6%
3 177411
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4314538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3000000
69.5%
6 638655
 
14.8%
1 498472
 
11.6%
3 177411
 
4.1%

reduction_amount
Real number (ℝ)

ZEROS 

Distinct1732
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6587664
Minimum0
Maximum350.26
Zeros2433302
Zeros (%)81.1%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:45.313282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile65
Maximum350.26
Range350.26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.732714
Coefficient of variation (CV)3.1135968
Kurtosis13.959094
Mean6.6587664
Median Absolute Deviation (MAD)0
Skewness3.6168733
Sum19976299
Variance429.84543
MonotonicityNot monotonic
2023-12-05T14:32:45.646634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2433302
81.1%
40 89376
 
3.0%
65 66068
 
2.2%
15 31950
 
1.1%
80 30562
 
1.0%
0.06 30284
 
1.0%
115 29156
 
1.0%
10 27820
 
0.9%
0.09 25775
 
0.9%
0.22 25416
 
0.8%
Other values (1722) 210291
 
7.0%
ValueCountFrequency (%)
0 2433302
81.1%
0.01 1010
 
< 0.1%
0.02 1477
 
< 0.1%
0.03 15576
 
0.5%
0.04 4304
 
0.1%
0.05 1359
 
< 0.1%
0.06 30284
 
1.0%
0.07 1078
 
< 0.1%
0.08 778
 
< 0.1%
0.09 25775
 
0.9%
ValueCountFrequency (%)
350.26 1
 
< 0.1%
330.26 1
 
< 0.1%
329.98 1
 
< 0.1%
247.21 1
 
< 0.1%
246.81 1
 
< 0.1%
241.65 1
 
< 0.1%
240 2
 
< 0.1%
210 7
< 0.1%
196.23 1
 
< 0.1%
193.92 1
 
< 0.1%

payment_amount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9828
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.130021
Minimum0
Maximum495.61
Zeros782730
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:45.920854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median65
Q375
95-th percentile125.65
Maximum495.61
Range495.61
Interquartile range (IQR)75

Descriptive statistics

Standard deviation42.299314
Coefficient of variation (CV)0.74040432
Kurtosis-0.40139383
Mean57.130021
Median Absolute Deviation (MAD)30
Skewness0.18971452
Sum1.7139006 × 108
Variance1789.232
MonotonicityNot monotonic
2023-12-05T14:32:46.171076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 1014155
33.8%
0 782730
26.1%
60 188424
 
6.3%
75 186831
 
6.2%
115 165193
 
5.5%
25 98796
 
3.3%
95 97902
 
3.3%
125 47306
 
1.6%
70 33029
 
1.1%
100 32203
 
1.1%
Other values (9818) 353431
 
11.8%
ValueCountFrequency (%)
0 782730
26.1%
0.01 6
 
< 0.1%
0.02 16
 
< 0.1%
0.03 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 7
 
< 0.1%
0.1 2
 
< 0.1%
0.11 1
 
< 0.1%
0.12 1
 
< 0.1%
0.14 1
 
< 0.1%
ValueCountFrequency (%)
495.61 1
 
< 0.1%
357.01 1
 
< 0.1%
345.43 1
 
< 0.1%
345 7
 
< 0.1%
335 36
< 0.1%
330 1
 
< 0.1%
264.32 1
 
< 0.1%
262.44 1
 
< 0.1%
261.99 1
 
< 0.1%
258.6 1
 
< 0.1%

amount_due
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11345
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.398221
Minimum0
Maximum522.49
Zeros2298078
Zeros (%)76.6%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:46.407354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile140.2
Maximum522.49
Range522.49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation50.728846
Coefficient of variation (CV)1.9216767
Kurtosis1.3157613
Mean26.398221
Median Absolute Deviation (MAD)0
Skewness1.6515214
Sum79194663
Variance2573.4158
MonotonicityNot monotonic
2023-12-05T14:32:46.703135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2298078
76.6%
75 150941
 
5.0%
140.24 68869
 
2.3%
125 41532
 
1.4%
134.55 31974
 
1.1%
65 31044
 
1.0%
70 30901
 
1.0%
115 26283
 
0.9%
95 26175
 
0.9%
195.79 19953
 
0.7%
Other values (11335) 274250
 
9.1%
ValueCountFrequency (%)
0 2298078
76.6%
0.01 1
 
< 0.1%
0.09 1
 
< 0.1%
0.17 1
 
< 0.1%
0.22 1
 
< 0.1%
0.24 1
 
< 0.1%
0.32 1
 
< 0.1%
0.4 1
 
< 0.1%
0.56 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
522.49 1
 
< 0.1%
502.04 2
 
< 0.1%
449.59 1
 
< 0.1%
449.31 1
 
< 0.1%
395.29 2
 
< 0.1%
372.77 2
 
< 0.1%
358.39 21
< 0.1%
345 6
 
< 0.1%
313.6 2
 
< 0.1%
313.45 2
 
< 0.1%

precinct
Real number (ℝ)

Distinct124
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.785967
Minimum0
Maximum809
Zeros1165
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:46.933952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q119
median52
Q384
95-th percentile114
Maximum809
Range809
Interquartile range (IQR)65

Descriptive statistics

Standard deviation36.717009
Coefficient of variation (CV)0.65817644
Kurtosis-0.81100835
Mean55.785967
Median Absolute Deviation (MAD)32
Skewness0.18100165
Sum1.673579 × 108
Variance1348.1388
MonotonicityNot monotonic
2023-12-05T14:32:47.215032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 144153
 
4.8%
114 136527
 
4.6%
6 118974
 
4.0%
13 91704
 
3.1%
9 77310
 
2.6%
1 73075
 
2.4%
10 66650
 
2.2%
70 66356
 
2.2%
115 62329
 
2.1%
46 61475
 
2.0%
Other values (114) 2101447
70.0%
ValueCountFrequency (%)
0 1165
 
< 0.1%
1 73075
2.4%
2 4
 
< 0.1%
3 7
 
< 0.1%
4 14
 
< 0.1%
5 43682
 
1.5%
6 118974
4.0%
7 34535
 
1.2%
8 1
 
< 0.1%
9 77310
2.6%
ValueCountFrequency (%)
809 4
 
< 0.1%
807 2
 
< 0.1%
804 1
 
< 0.1%
803 1
 
< 0.1%
620 1
 
< 0.1%
619 1
 
< 0.1%
174 1
 
< 0.1%
163 4
 
< 0.1%
123 374
 
< 0.1%
122 1417
< 0.1%

county
Categorical

Distinct9
Distinct (%)< 0.1%
Missing3861
Missing (%)0.1%
Memory size22.9 MiB
NY
1108470 
K
777043 
Q
565080 
BX
401607 
Kings
 
75504
Other values (4)
 
68435

Length

Max length5
Median length2
Mean length1.6720182
Min length1

Characters and Unicode

Total characters5009599
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowQ
3rd rowQ
4th rowQ
5th rowQ

Common Values

ValueCountFrequency (%)
NY 1108470
36.9%
K 777043
25.9%
Q 565080
18.8%
BX 401607
 
13.4%
Kings 75504
 
2.5%
Bronx 39601
 
1.3%
Qns 21471
 
0.7%
R 7356
 
0.2%
Rich 7
 
< 0.1%
(Missing) 3861
 
0.1%

Length

2023-12-05T14:32:47.547300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T14:32:47.818797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
ny 1108470
37.0%
k 777043
25.9%
q 565080
18.9%
bx 401607
 
13.4%
kings 75504
 
2.5%
bronx 39601
 
1.3%
qns 21471
 
0.7%
r 7356
 
0.2%
rich 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 1108470
22.1%
Y 1108470
22.1%
K 852547
17.0%
Q 586551
11.7%
B 441208
 
8.8%
X 401607
 
8.0%
n 136576
 
2.7%
s 96975
 
1.9%
i 75511
 
1.5%
g 75504
 
1.5%
Other values (6) 126180
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4506216
90.0%
Lowercase Letter 503383
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 136576
27.1%
s 96975
19.3%
i 75511
15.0%
g 75504
15.0%
r 39601
 
7.9%
o 39601
 
7.9%
x 39601
 
7.9%
c 7
 
< 0.1%
h 7
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1108470
24.6%
Y 1108470
24.6%
K 852547
18.9%
Q 586551
13.0%
B 441208
 
9.8%
X 401607
 
8.9%
R 7363
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5009599
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1108470
22.1%
Y 1108470
22.1%
K 852547
17.0%
Q 586551
11.7%
B 441208
 
8.8%
X 401607
 
8.0%
n 136576
 
2.7%
s 96975
 
1.9%
i 75511
 
1.5%
g 75504
 
1.5%
Other values (6) 126180
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5009599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1108470
22.1%
Y 1108470
22.1%
K 852547
17.0%
Q 586551
11.7%
B 441208
 
8.8%
X 401607
 
8.0%
n 136576
 
2.7%
s 96975
 
1.9%
i 75511
 
1.5%
g 75504
 
1.5%
Other values (6) 126180
 
2.5%

issuing_agency
Categorical

IMBALANCE 

Distinct23
Distinct (%)< 0.1%
Missing26
Missing (%)< 0.1%
Memory size22.9 MiB
TRAFFIC
2768450 
DEPARTMENT OF SANITATION
 
195036
POLICE DEPARTMENT
 
29894
OTHER/UNKNOWN AGENCIES
 
3726
PARKS DEPARTMENT
 
1964
Other values (18)
 
904

Length

Max length35
Median length7
Mean length8.2339134
Min length7

Characters and Unicode

Total characters24701526
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTRAFFIC
2nd rowTRAFFIC
3rd rowTRAFFIC
4th rowTRAFFIC
5th rowTRAFFIC

Common Values

ValueCountFrequency (%)
TRAFFIC 2768450
92.3%
DEPARTMENT OF SANITATION 195036
 
6.5%
POLICE DEPARTMENT 29894
 
1.0%
OTHER/UNKNOWN AGENCIES 3726
 
0.1%
PARKS DEPARTMENT 1964
 
0.1%
ROOSEVELT ISLAND SECURITY 183
 
< 0.1%
HEALTH AND HOSPITAL CORP. POLICE 175
 
< 0.1%
LONG ISLAND RAILROAD 124
 
< 0.1%
PARKING CONTROL UNIT 86
 
< 0.1%
CON RAIL 65
 
< 0.1%
Other values (13) 271
 
< 0.1%

Length

2023-12-05T14:32:48.119125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
traffic 2768450
80.8%
department 226945
 
6.6%
of 195045
 
5.7%
sanitation 195036
 
5.7%
police 30092
 
0.9%
other/unknown 3726
 
0.1%
agencies 3726
 
0.1%
parks 1975
 
0.1%
island 307
 
< 0.1%
health 185
 
< 0.1%
Other values (34) 2120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 5732075
23.2%
T 3617578
14.6%
A 3392701
13.7%
I 3193736
12.9%
R 3002565
12.2%
C 2802915
11.3%
N 633095
 
2.6%
E 496029
 
2.0%
O 429196
 
1.7%
427633
 
1.7%
Other values (16) 974003
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24269992
98.3%
Space Separator 427633
 
1.7%
Other Punctuation 3901
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5732075
23.6%
T 3617578
14.9%
A 3392701
14.0%
I 3193736
13.2%
R 3002565
12.4%
C 2802915
11.5%
N 633095
 
2.6%
E 496029
 
2.0%
O 429196
 
1.8%
P 259501
 
1.1%
Other values (13) 710601
 
2.9%
Other Punctuation
ValueCountFrequency (%)
/ 3726
95.5%
. 175
 
4.5%
Space Separator
ValueCountFrequency (%)
427633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24269992
98.3%
Common 431534
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5732075
23.6%
T 3617578
14.9%
A 3392701
14.0%
I 3193736
13.2%
R 3002565
12.4%
C 2802915
11.5%
N 633095
 
2.6%
E 496029
 
2.0%
O 429196
 
1.8%
P 259501
 
1.1%
Other values (13) 710601
 
2.9%
Common
ValueCountFrequency (%)
427633
99.1%
/ 3726
 
0.9%
. 175
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24701526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5732075
23.2%
T 3617578
14.6%
A 3392701
13.7%
I 3193736
12.9%
R 3002565
12.2%
C 2802915
11.3%
N 633095
 
2.6%
E 496029
 
2.0%
O 429196
 
1.7%
427633
 
1.7%
Other values (16) 974003
 
3.9%

summons_image
Text

UNIQUE 

Distinct3000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.9 MiB
2023-12-05T14:32:50.829571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length162
Median length162
Mean length162
Min length162

Characters and Unicode

Total characters486000000
Distinct characters65
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3000000 ?
Unique (%)100.0%

Sample

1st row{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQUkZGM1RXYzlQUT09&locationName=_____________________', 'description': 'View Summons'}
2nd row{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQUkdzd1RYYzlQUT09&locationName=_____________________', 'description': 'View Summons'}
3rd row{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBScmQwMXFWWHBOUkZrd1RrRTlQUT09&locationName=_____________________', 'description': 'View Summons'}
4th row{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQVkVsM1RVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}
5th row{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQVkdNeFRYYzlQUT09&locationName=_____________________', 'description': 'View Summons'}
ValueCountFrequency (%)
url 3000000
20.0%
description 3000000
20.0%
view 3000000
20.0%
summons 3000000
20.0%
http://nycserv.nyc.gov/nycservweb/showimage?searchid=vdbsbk5vouvtvfzovkzfelrrrtlqut09&locationname 1
 
< 0.1%
http://nycserv.nyc.gov/nycservweb/showimage?searchid=vdbsbmvvntzhm2xpukzfmvqwrtlqut09&locationname 1
 
< 0.1%
http://nycserv.nyc.gov/nycservweb/showimage?searchid=vdbsbk5vouvwwgxoukvwnvqwrtlqut09&locationname 1
 
< 0.1%
http://nycserv.nyc.gov/nycservweb/showimage?searchid=vdbsbmvvntzhm2xpukuxm1qxrtlqut09&locationname 1
 
< 0.1%
http://nycserv.nyc.gov/nycservweb/showimage?searchid=vdbscmqwmxfwwhboukzrd1rrrtlqut09&locationname 1
 
< 0.1%
http://nycserv.nyc.gov/nycservweb/showimage?searchid=vdbsbmvvntzaelzqvkvsm1rvrtlqut09&locationname 1
 
< 0.1%
Other values (2999994) 2999994
20.0%
2023-12-05T14:32:53.565883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 63000000
 
13.0%
e 25445702
 
5.2%
' 24000000
 
4.9%
o 18000000
 
3.7%
r 16215809
 
3.3%
c 15798057
 
3.3%
n 15000000
 
3.1%
V 14931288
 
3.1%
m 13108313
 
2.7%
t 12632881
 
2.6%
Other values (55) 267867950
55.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 227783714
46.9%
Uppercase Letter 101594853
20.9%
Connector Punctuation 63000000
 
13.0%
Other Punctuation 60000000
 
12.3%
Space Separator 12000000
 
2.5%
Decimal Number 9621433
 
2.0%
Math Symbol 6000000
 
1.2%
Close Punctuation 3000000
 
0.6%
Open Punctuation 3000000
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25445702
 
11.2%
o 18000000
 
7.9%
r 16215809
 
7.1%
c 15798057
 
6.9%
n 15000000
 
6.6%
m 13108313
 
5.8%
t 12632881
 
5.5%
s 12632788
 
5.5%
a 12360617
 
5.4%
i 12000000
 
5.3%
Other values (14) 74589547
32.7%
Uppercase Letter
ValueCountFrequency (%)
V 14931288
14.7%
S 12138186
11.9%
N 8261755
 
8.1%
Q 6025613
 
5.9%
I 6000000
 
5.9%
W 5860271
 
5.8%
T 5818568
 
5.7%
D 5768434
 
5.7%
U 5339310
 
5.3%
Y 4965612
 
4.9%
Other values (13) 26485816
26.1%
Other Punctuation
ValueCountFrequency (%)
' 24000000
40.0%
/ 12000000
20.0%
: 9000000
 
15.0%
. 6000000
 
10.0%
, 3000000
 
5.0%
& 3000000
 
5.0%
? 3000000
 
5.0%
Decimal Number
ValueCountFrequency (%)
0 3522568
36.6%
9 3000000
31.2%
1 1982031
20.6%
5 758954
 
7.9%
2 264851
 
2.8%
3 93029
 
1.0%
Connector Punctuation
ValueCountFrequency (%)
_ 63000000
100.0%
Space Separator
ValueCountFrequency (%)
12000000
100.0%
Math Symbol
ValueCountFrequency (%)
= 6000000
100.0%
Close Punctuation
ValueCountFrequency (%)
} 3000000
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 3000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 329378567
67.8%
Common 156621433
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25445702
 
7.7%
o 18000000
 
5.5%
r 16215809
 
4.9%
c 15798057
 
4.8%
n 15000000
 
4.6%
V 14931288
 
4.5%
m 13108313
 
4.0%
t 12632881
 
3.8%
s 12632788
 
3.8%
a 12360617
 
3.8%
Other values (37) 173253112
52.6%
Common
ValueCountFrequency (%)
_ 63000000
40.2%
' 24000000
 
15.3%
/ 12000000
 
7.7%
12000000
 
7.7%
: 9000000
 
5.7%
= 6000000
 
3.8%
. 6000000
 
3.8%
0 3522568
 
2.2%
, 3000000
 
1.9%
} 3000000
 
1.9%
Other values (8) 15098865
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 486000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 63000000
 
13.0%
e 25445702
 
5.2%
' 24000000
 
4.9%
o 18000000
 
3.7%
r 16215809
 
3.3%
c 15798057
 
3.3%
n 15000000
 
3.1%
V 14931288
 
3.1%
m 13108313
 
2.7%
t 12632881
 
2.6%
Other values (55) 267867950
55.1%

violation_status
Categorical

MISSING 

Distinct13
Distinct (%)< 0.1%
Missing2330658
Missing (%)77.7%
Memory size22.9 MiB
HEARING HELD-GUILTY
264524 
HEARING HELD-GUILTY REDUCTION
217000 
HEARING HELD-NOT GUILTY
125673 
HEARING PENDING
44841 
APPEAL AFFIRMED
 
6846
Other values (8)
 
10458

Length

Max length29
Median length26
Mean length22.664424
Min length15

Characters and Unicode

Total characters15170251
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHEARING HELD-NOT GUILTY
2nd rowHEARING HELD-GUILTY REDUCTION
3rd rowAPPEAL AFFIRMED
4th rowHEARING HELD-NOT GUILTY
5th rowHEARING HELD-NOT GUILTY

Common Values

ValueCountFrequency (%)
HEARING HELD-GUILTY 264524
 
8.8%
HEARING HELD-GUILTY REDUCTION 217000
 
7.2%
HEARING HELD-NOT GUILTY 125673
 
4.2%
HEARING PENDING 44841
 
1.5%
APPEAL AFFIRMED 6846
 
0.2%
HEARING ADJOURNMENT 4827
 
0.2%
ADMIN REDUCTION 2424
 
0.1%
ADMIN CLAIM GRANTED 1513
 
0.1%
APPEAL REVERSED 818
 
< 0.1%
ADMIN CLAIM DENIED 493
 
< 0.1%
Other values (3) 383
 
< 0.1%
(Missing) 2330658
77.7%

Length

2023-12-05T14:32:53.819358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hearing 657007
39.0%
held-guilty 481524
28.6%
reduction 219424
 
13.0%
held-not 125673
 
7.5%
guilty 125673
 
7.5%
pending 44841
 
2.7%
appeal 7905
 
0.5%
affirmed 6846
 
0.4%
adjournment 4827
 
0.3%
admin 4430
 
0.3%
Other values (7) 5213
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 1553809
10.2%
I 1542388
10.2%
G 1310558
 
8.6%
H 1264346
 
8.3%
L 1224447
 
8.1%
N 1108640
 
7.3%
1014021
 
6.7%
T 959060
 
6.3%
D 891506
 
5.9%
R 891395
 
5.9%
Other values (13) 3410081
22.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13548891
89.3%
Space Separator 1014021
 
6.7%
Dash Punctuation 607339
 
4.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1553809
11.5%
I 1542388
11.4%
G 1310558
9.7%
H 1264346
9.3%
L 1224447
9.0%
N 1108640
8.2%
T 959060
7.1%
D 891506
6.6%
R 891395
6.6%
U 831448
6.1%
Other values (11) 1971294
14.5%
Space Separator
ValueCountFrequency (%)
1014021
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 607339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13548891
89.3%
Common 1621360
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1553809
11.5%
I 1542388
11.4%
G 1310558
9.7%
H 1264346
9.3%
L 1224447
9.0%
N 1108640
8.2%
T 959060
7.1%
D 891506
6.6%
R 891395
6.6%
U 831448
6.1%
Other values (11) 1971294
14.5%
Common
ValueCountFrequency (%)
1014021
62.5%
- 607339
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15170251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1553809
10.2%
I 1542388
10.2%
G 1310558
 
8.6%
H 1264346
 
8.3%
L 1224447
 
8.1%
N 1108640
 
7.3%
1014021
 
6.7%
T 959060
 
6.3%
D 891506
 
5.9%
R 891395
 
5.9%
Other values (13) 3410081
22.5%

Interactions

2023-12-05T14:32:04.336146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:43.926187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:48.038715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:51.927539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:56.020012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:00.301748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:05.004256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:44.627243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:48.655857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:52.663158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:56.846527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:00.987002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:05.660474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:45.308333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:49.295022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:53.324765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:57.478113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:01.650505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:06.364310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:45.972515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:49.969221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:53.956685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:58.176696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:02.295402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:07.008749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:46.657130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:50.599532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:54.591988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:58.833965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:02.952016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:07.639493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:47.323910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:51.275050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:55.281001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:31:59.571888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T14:32:03.676069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T14:32:54.008662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
amount_duecountyfine_amountissuing_agencypayment_amountpenalty_amountprecinctreduction_amountsummons_numberviolationviolation_status
amount_due1.0000.0700.0070.026-0.6900.4480.015-0.2500.1470.1520.296
county0.0701.000-0.0240.2940.0210.1070.1520.065-0.1040.1890.151
fine_amount0.007-0.0241.0000.0780.2190.034-0.2860.157-0.0040.5350.383
issuing_agency0.0260.2940.0781.0000.0180.050-0.0630.0500.4620.1010.067
payment_amount-0.6900.0210.2190.0181.0000.3320.0140.029-0.0750.2370.326
penalty_amount0.4480.1070.0340.0500.3321.0000.0750.0350.1400.0670.233
precinct0.0150.152-0.286-0.0630.0140.0751.000-0.210-0.0480.1020.094
reduction_amount-0.2500.0650.1570.0500.0290.035-0.2101.000-0.0480.1810.324
summons_number0.147-0.104-0.0040.462-0.0750.140-0.048-0.0481.0000.1380.134
violation0.1520.1890.5350.1010.2370.0670.1020.1810.1381.0000.104
violation_status0.2960.1510.3830.0670.3260.2330.0940.3240.1340.1041.000

Missing values

2023-12-05T14:32:09.273344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T14:32:13.946761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-05T14:32:24.364219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

platestatelicense_typesummons_numberissue_dateviolation_timeviolationjudgment_entry_datefine_amountpenalty_amountreduction_amountpayment_amountamount_dueprecinctcountyissuing_agencysummons_imageviolation_status
0KGY4654NYPAS882789840205/21/202107:44ADOUBLE PARKING07/28/2022115600.000.00195.79101QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQUkZGM1RXYzlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
1RBK1301NCPAS882789894305/25/202106:35ADOUBLE PARKING07/28/2022115600.000.00195.80100QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQUkdzd1RYYzlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2KNB3224NYPAS890253064410/07/202112:12PNO PARKING-STREET CLEANING07/28/202265600.00137.970.00102QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBScmQwMXFWWHBOUkZrd1RrRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
3KEW1301NYPAS882789920005/25/202108:55ANO PARKING-STREET CLEANING07/28/202265600.000.00140.24101QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQVkVsM1RVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
48VUB416CAPAS882789975305/26/202108:46ANO PARKING-STREET CLEANING07/28/202265600.000.00140.24107QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQVkdNeFRYYzlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
5JLA9682NYPAS882789980705/26/202108:59ANO PARKING-STREET CLEANING07/28/202265600.000.00140.24107QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQVkdkM1RuYzlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
6UHU2779VAPAS882789996005/27/202102:06ANO PARKING-DAY/TIME LIMITS07/28/202260600.000.00134.55107QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbmVVNTZaelZQVkdzeVRVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
72962540INPAS889827119006/23/202103:59PNO PARKING-DAY/TIME LIMITSNaN65065.000.000.001NYTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbk5VOUVTVE5OVkVVMVRVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}HEARING HELD-NOT GUILTY
876239MDNYCOM889829147409/22/202111:50ANO PARKING-DAY/TIME LIMITS10/20/202265600.16134.350.001NYTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbk5VOUVTVFZOVkZFelRrRTlQUT09&locationName=_____________________', 'description': 'View Summons'}HEARING HELD-GUILTY REDUCTION
933163NANYCOM889831668905/11/202112:01PNO PARKING-DAY/TIME LIMITSNaN6500.0065.000.006NYTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDBSbk5VOUVUWGhPYWxrMFQxRTlQUT09&locationName=_____________________', 'description': 'View Summons'}APPEAL AFFIRMED
platestatelicense_typesummons_numberissue_dateviolation_timeviolationjudgment_entry_datefine_amountpenalty_amountreduction_amountpayment_amountamount_dueprecinctcountyissuing_agencysummons_imageviolation_status
2999990B80L07YCNYOMT907789947909/21/202300:25ANO PARKING-STREET CLEANINGNaN65100.00.075.0103QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wNTZaelZQVkZFelQxRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999991G50PPPNJPAS907789949209/21/202300:33ANO PARKING-STREET CLEANINGNaN65300.00.095.0103QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wNTZaelZQVkZFMVRXYzlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999992JRV2751NYPAS907789951009/21/202300:40ANO PARKING-STREET CLEANINGNaN65300.00.095.0113QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wNTZaelZQVkZWNFRVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999993LBD5860NYPAS907789954609/21/202301:47ANO PARKING-DAY/TIME LIMITSNaN60300.00.090.0103QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wNTZaelZQVkZVd1RtYzlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999994HKX5517NYPAS907973065809/15/202311:57ANO PARKING-STREET CLEANINGNaN65300.095.00.077KTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wOVVZM3BOUkZreFQwRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999995DA71826ILPAS907973239409/22/202312:02PNO PARKING-STREET CLEANINGNaN65100.075.00.088KTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wOVVZM3BOYWswMVRrRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999996BEZ1176NYPAS908654011910/15/202302:22PNO PARKING-EXC. HOTEL LOADINGNaN11500.0115.00.013NYTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk5FNXFWVEJOUkVWNFQxRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999997GRA6091NYPAS907973514009/15/202308:08ANO PARKING-STREET CLEANINGNaN65300.095.00.0107QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wOVVZM3BPVkVVd1RVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999998LPZ4310PAPAS907790001909/08/202312:43PNO PARKING-STREET CLEANINGNaN65300.00.095.0102QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wNTZhM2ROUkVGNFQxRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN
2999999HEG9632NYPAS907790002009/08/202312:46PNO PARKING-STREET CLEANINGNaN65300.00.095.0102QTRAFFIC{'url': 'http://nycserv.nyc.gov/NYCServWeb/ShowImage?searchID=VDFSQk0wNTZhM2ROUkVGNVRVRTlQUT09&locationName=_____________________', 'description': 'View Summons'}NaN